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Zhiguang Wang

4 papers hereh-index 5521 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • cs.LG1
same name
  • Zhiguang Wang — 5 papers
  • Zhiguang Wang — 4 papers, h 21
  • Zhiguang Wang — 2 papers, h 30
  • Zhiguang Wang — 2 papers, h 5
  • Zhiguang Wang — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedGated Transformer Networks for Multivariate Time Series Classification

100 citations · 104 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CL2021★ 2 cited

Zero-Shot Dialogue State Tracking via Cross-Task Transfer

Zhaojiang Lin, Bing Liu, Andrea Madotto +8

Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data. In…

cs.CL2021

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking

Zhaojiang Lin, Bing Liu, Seungwhan Moon +7

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data. In this paper,…

cs.LG2021★ 100 cited

Gated Transformer Networks for Multivariate Time Series Classification

Minghao Liu, Shengqi Ren, Siyuan Ma +4

Deep learning model (primarily convolutional networks and LSTM) for time series classification has been studied broadly by the community with the wide applications in different dom…

cs.CL2020★ 2 cited

Continual Learning in Task-Oriented Dialogue Systems

Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou +6

Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.…

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